
!pip install jovian opendatasets --upgrade --quiet


dataset_url='https://www.kaggle.com/datasets/awadhi123/indian-election-dataset?select=indian-state-level-election.csv'


import opendatasets as od
od.download(dataset_url)

Skipping, found downloaded files in ".\indian-election-dataset" (use force=True to force download)


data_dir = './indian-election-dataset'


import os
os.listdir(data_dir)

['indian-national-level-election.csv', 'indian-state-level-election.csv']


project_name = "zerotopandas-course-project-starter"


!pip install jovian --upgrade -q


import pandas as pd


# Loading the dataset
ind_elec_state_df =pd.read_csv('indian-state-level-election.csv')


# reading the dataset
ind_elec_state_df


# getting info about dataset
ind_elec_state_df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 327294 entries, 0 to 327293
Data columns (total 11 columns):
 #   Column      Non-Null Count   Dtype  
---  ------      --------------   -----  
 0   st_name     327294 non-null  object 
 1   year        327294 non-null  float64
 2   ac_no       327294 non-null  int64  
 3   ac_name     311211 non-null  object 
 4   ac_type     327294 non-null  object 
 5   cand_name   327294 non-null  object 
 6   cand_sex    321992 non-null  object 
 7   partyname   327294 non-null  object 
 8   partyabbre  324588 non-null  object 
 9   totvotpoll  327287 non-null  float64
 10  electors    327294 non-null  int64  
dtypes: float64(2), int64(2), object(7)
memory usage: 27.5+ MB


# getting random sample dataset
ind_elec_state_df.sample(10)


type(ind_elec_state_df)

pandas.core.frame.DataFrame


# Describing the dataset
ind_elec_state_df.describe()


# Getting total Rows and Column of dataset
ind_elec_state_df.shape

(327294, 11)


# Getting total Column in dataset
ind_elec_state_df.columns

Index(['st_name', 'year', 'ac_no', 'ac_name', 'ac_type', 'cand_name',
       'cand_sex', 'partyname', 'partyabbre', 'totvotpoll', 'electors'],
      dtype='object')


# Parsing year
# Reading the CSV files of State Level Election
ind_elec_state_df = pd.read_csv('indian-state-level-election.csv', parse_dates=['year'])


ind_elec_state_df


import numpy as np


ind_elec_state_df


ind_elec_state_df.nunique()

st_name           30
year              37
ac_no            425
ac_name         6247
ac_type            5
cand_name     220531
cand_sex           3
partyname       1663
partyabbre      1285
totvotpoll     53067
electors       31291
dtype: int64


# Total States
# ind_elec_state_df['st_name'].nunique()
# or
ind_elec_state_df.st_name.nunique()

30


# Listing Total States 
ind_elec_state_df.st_name.unique()

array(['Andhra Pradesh', 'Arunachal Pradesh', 'Assam', 'Bihar',
       'Chhattisgarh', 'Goa', 'Gujarat', 'Haryana', 'Himachal Pradesh',
       'Jammu & Kashmir', 'Jharkhand', 'Karnataka', 'Kerala',
       'Madhya Pradesh', 'Maharashtra', 'Manipur', 'Meghalaya', 'Mizoram',
       'Nagaland', 'National Capital Territory Of Delhi', 'Odisha',
       'Puducherry', 'Punjab', 'Rajasthan', 'Sikkim', 'Tamil Nadu',
       'Tripura', 'Uttar Pradesh', 'Uttarakhand', 'West Bengal'],
      dtype=object)


import ipywidgets as widgets
from IPython.display import display, HTML

# Assuming `ind_elec_state_df` is your DataFrame
state_dropdown = widgets.Dropdown(options=ind_elec_state_df.st_name.unique(), description='Select State:')
output = widgets.Output()

def on_state_change(change):
    selected_state = change.new
    
    output.clear_output()  # Clear previous output
    
    with output:
        state_candidate_data = ind_elec_state_df[ind_elec_state_df['st_name'] == selected_state][[
            'cand_name', 'cand_sex',  'partyabbre', 'totvotpoll', 'electors'
        ]]
        
        if not state_candidate_data.empty:
            display(HTML(state_candidate_data.to_html(index=False)))
        else:
            print("No candidate data available for the selected state.")

state_dropdown.observe(on_state_change, names='value')

display(state_dropdown, output)


import ipywidgets as widgets
from IPython.display import display, HTML

# Assuming `ind_elec_state_df` is your DataFrame
party_dropdown = widgets.Dropdown(options=ind_elec_state_df['partyname'].unique(), description='Select Party:')
state_dropdown = widgets.Dropdown(options=ind_elec_state_df['st_name'].unique(), description='Select State:')
output = widgets.Output()

def on_party_change(change):
    selected_party = party_dropdown.value
    selected_state = state_dropdown.value  # You forgot to retrieve the value here
    
    output.clear_output()  # Clear previous output
    
    with output:
        party_data = ind_elec_state_df[(ind_elec_state_df['partyname'] == selected_party) & (ind_elec_state_df['st_name'] == selected_state)][['cand_name', 'cand_sex','st_name','year','ac_name','ac_type']]
        
        if not party_data.empty:
            display(HTML(party_data.to_html(index=False)))
        else:
            print("No data available for the selected party and state.")

party_dropdown.observe(on_party_change, names='value')

display(widgets.VBox([party_dropdown, state_dropdown, output]))  # Group the widgets in a VBox


# Counting total Assembly in State
ind_elec_state_df.ac_name.nunique()

6247


# Displaying Unique Assembly Name
import pandas as pd
from IPython.display import display
unique_ac_names = ind_elec_state_df['ac_name'].unique()
with pd.option_context('display.max_rows', None):
    display(pd.DataFrame({'Unique Assembly Name': unique_ac_names}))


# State Unique Parties
print('Total number of unique parties in a state election is {}'.format(ind_elec_state_df.partyname.nunique()))

Total number of unique parties in a state election is 1663


ind_elec_state_df.partyname.unique()

array(['Independent', 'Indian National Congress',
       'Indian National Congress (I)', ..., 'STPI', 'DSP(P)', 'JHAP'],
      dtype=object)


ind_elec_state_df.drop('partyabbre',axis = 1,inplace = True)


# Dataset After Dropping Partyname column
ind_elec_state_df


# <!-- New Table Shape -->
ind_elec_state_df.shape

(327294, 10)


import jovian


jovian.commit()

[jovian] Updating notebook "abhishekgupta022/indian-election-dataset" on https://jovian.com/
[jovian] Committed successfully! https://jovian.com/abhishekgupta022/indian-election-dataset

'https://jovian.com/abhishekgupta022/indian-election-dataset'


ind_elec_national_df = pd.read_csv('indian-national-level-election.csv')


ind_elec_national_df


# Getting Info of Dataset
ind_elec_national_df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 73081 entries, 0 to 73080
Data columns (total 11 columns):
 #   Column      Non-Null Count  Dtype 
---  ------      --------------  ----- 
 0   st_name     73081 non-null  object
 1   year        73081 non-null  int64 
 2   pc_no       73081 non-null  int64 
 3   pc_name     73081 non-null  object
 4   pc_type     65011 non-null  object
 5   cand_name   73081 non-null  object
 6   cand_sex    72539 non-null  object
 7   partyname   73081 non-null  object
 8   partyabbre  73081 non-null  object
 9   totvotpoll  73081 non-null  int64 
 10  electors    73081 non-null  int64 
dtypes: int64(4), object(7)
memory usage: 6.1+ MB


# Sample of Database
ind_elec_national_df.sample(5)


ind_elec_national_df.drop('partyabbre',axis=1,inplace=True)


ind_elec_national_df.drop('pc_no',axis=1,inplace=True)


ind_elec_national_df


# Getting Shape Of dataset
ind_elec_national_df.shape

(73081, 9)


# Getting Types of Dataset
type(ind_elec_national_df)

pandas.core.frame.DataFrame


ind_elec_national_df.columns

Index(['st_name', 'year', 'pc_name', 'pc_type', 'cand_name', 'cand_sex',
       'partyname', 'totvotpoll', 'electors'],
      dtype='object')


# Dataset VAlues
ind_elec_national_df.nunique()

st_name          43
year             11
pc_name         769
pc_type           4
cand_name     56601
cand_sex          3
partyname      1424
totvotpoll    29173
electors       5911
dtype: int64


import pandas as pd
description = ind_elec_national_df.describe()
formatted_description = description.applymap(lambda x: "{:.2f}".format(x) if isinstance(x, (float, int)) else x)
print(formatted_description)

           year totvotpoll    electors
count  73081.00   73081.00    73081.00
mean    1996.78   49834.76  1122277.22
std       10.43  104893.32   356004.91
min     1977.00       0.00    19471.00
25%     1989.00     872.00   912985.00
50%     1996.00    2743.00  1099503.00
75%     2004.00   19185.00  1329086.00
max     2014.00  863358.00  3368399.00


import ipywidgets as widgets
from IPython.display import display, HTML

# Assuming `ind_elec_national_df` is your DataFrame
state_dropdown = widgets.Dropdown(options=ind_elec_national_df['st_name'].unique(), description='Select State:')
output = widgets.Output()

def on_state_change(change):
    selected_state = change.new
    
    output.clear_output()  # Clear previous output
    
    with output:
        national_candidate_data = ind_elec_national_df[ind_elec_national_df['st_name'] == selected_state][[
            'cand_name', 'cand_sex','year', 'partyname', 'totvotpoll', 'electors'
        ]]
        
        if not national_candidate_data.empty:
            print(f"{national_candidate_data['cand_name'].nunique()} Records Found")
            display(HTML(national_candidate_data.to_html(index=False)))
            
        else:
            print("No candidate data available for the selected state.")

state_dropdown.observe(on_state_change, names='value')

display(state_dropdown)
display(output)  # Display the output widget


import ipywidgets as widgets
from IPython.display import display, HTML

# Assuming `ind_elec_national_df` is your DataFrame
party_dropdown = widgets.Dropdown(options=ind_elec_national_df['partyname'].unique(), description='Select Party:')
state_dropdown = widgets.Dropdown(options=ind_elec_national_df['st_name'].unique(), description='Select State:')
year_dropdown = widgets.Dropdown(options=ind_elec_national_df['year'].unique(), description='Select Year:')  # Updated year options
output = widgets.Output()

def on_filter_change(change):
    selected_party = party_dropdown.value
    selected_state = state_dropdown.value
    selected_year  = year_dropdown.value
    
    output.clear_output()  # Clear previous output
    
    with output:
        
        
        national_candidate_data = ind_elec_national_df[
            (ind_elec_national_df['partyname'] == selected_party) &
            (ind_elec_national_df['st_name'] == selected_state) &
            (ind_elec_national_df['year'] == selected_year)]
        [['cand_name', 'cand_sex', 'pc_name', 'pc_type']]
        
        if not national_candidate_data.empty:
            print(f"{national_candidate_data['cand_name'].nunique()} Records Found")
            display(HTML(national_candidate_data.to_html(index=False)))
        else:
            print("No data available for the selected party, state, and year.")

party_dropdown.observe(on_filter_change, names='value')
state_dropdown.observe(on_filter_change, names='value')
year_dropdown.observe(on_filter_change,  names='value')

display(widgets.VBox([party_dropdown, state_dropdown, year_dropdown, output]))


import matplotlib
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
sns.set_style('darkgrid')
matplotlib.rcParams['font.size'] = 14
matplotlib.rcParams['figure.figsize'] = (9, 5)
matplotlib.rcParams['figure.facecolor'] = '#00000000'


ind_elec_state_df


print(ind_elec_state_df.dtypes)

st_name        object
year           object
ac_no           int64
ac_name        object
ac_type        object
cand_name      object
cand_sex       object
partyname      object
totvotpoll    float64
electors        int64
dtype: object


ind_elec_state_df.electors = ind_elec_state_df.electors.astype(int)
print(ind_elec_state_df.dtypes)

st_name        object
year           object
ac_no           int64
ac_name        object
ac_type        object
cand_name      object
cand_sex       object
partyname      object
totvotpoll    float64
electors        int32
dtype: object


ind_elec_state_df['year'] = ind_elec_state_df['year'].astype(str).str.split('.').str[0].astype(int)
print(ind_elec_state_df.dtypes)

st_name        object
year            int32
ac_no           int64
ac_name        object
ac_type        object
cand_name      object
cand_sex       object
partyname      object
totvotpoll    float64
electors        int32
dtype: object


ind_elec_national_df


ind_elec_national_df['year'] = ind_elec_national_df['year'].astype(str).str.split('.').str[0].astype(int)
print(ind_elec_national_df.dtypes)

st_name       object
year           int32
pc_name       object
pc_type       object
cand_name     object
cand_sex      object
partyname     object
totvotpoll     int64
electors       int64
dtype: object


ind_elec_national_df.electors = ind_elec_national_df.totvotpoll.astype(float)
print(ind_elec_national_df.dtypes)

st_name        object
year            int32
pc_name        object
pc_type        object
cand_name      object
cand_sex       object
partyname      object
totvotpoll      int64
electors      float64
dtype: object


import ipywidgets as widgets
from IPython.display import display
import matplotlib.pyplot as plt
import seaborn as sns

ind_elec_state_df['year'] = ind_elec_state_df['year'].astype(int)
# Create year dropdown widget
year_dropdown = widgets.Dropdown(options=ind_elec_state_df['year'].unique(), description='Select Year:')

# Define an output widget
output = widgets.Output()

# Create a function to update the scatterplot based on the selected year
def update_scatterplot(change):
    selected_year = year_dropdown.value
    
    with output:
        output.clear_output()
        plt.figure(figsize=(10, 6))
        plt.title(f"INDIAN STATE ELECTION DATASET TOTAL VOTE vs Number of registered voters ({selected_year})")
        sns.scatterplot(x=ind_elec_state_df[ind_elec_state_df['year'] == selected_year].ac_type,
                        y=ind_elec_state_df[ind_elec_state_df['year'] == selected_year].totvotpoll)
        plt.xlabel('Assembly constituency reservation status')
        plt.ylabel('Votes received')
        plt.show()

year_dropdown.observe(update_scatterplot, names='value')

# Display widgets
display(year_dropdown)
display(output)


import ipywidgets as widgets
from IPython.display import display
import matplotlib.pyplot as plt
import seaborn as sns
# Convert 'year' column to integers
ind_elec_national_df['year'] = ind_elec_national_df['year'].astype(int)

# Create year dropdown widget
year_dropdown = widgets.Dropdown(options=ind_elec_national_df['year'].unique(), description='Select Year:')

# Define an output widget
output = widgets.Output()

# Create a function to update the scatterplot based on the selected year
def update_scatterplot(change):
    selected_year = year_dropdown.value
    
    with output:
        output.clear_output()
        plt.figure(figsize=(10, 6))
        plt.title(f"INDIAN NATIONAL ELECTION DATASET TOTAL VOTE POLL vs Number of registered voters ({selected_year})")
        sns.scatterplot(x=ind_elec_national_df[ind_elec_national_df['year'] == selected_year].pc_type,
                        y=ind_elec_national_df[ind_elec_national_df['year'] == selected_year].totvotpoll)
        plt.xlabel('Parliamentary constituency reservation status')
        plt.ylabel('Votes received')
        plt.show()

year_dropdown.observe(update_scatterplot, names='value')

# Display widgets
display(year_dropdown)
display(output)


# AXIS CHANGED FOR STATE
import ipywidgets as widgets
from IPython.display import display
import matplotlib.pyplot as plt
import seaborn as sns
assembly_dropdown = widgets.Dropdown(options = ind_elec_state_df['ac_type'].unique(),description = 'Select Category')
output = widgets.Output()
def scatterplot_category(change):
    selected_category = assembly_dropdown.value
    with output:
        output.clear_output()
        plt.title(f"INDIAN NATIONAL ELECTION DATASET TOTAL VOTE POLL vs Number of registered voters ({selected_category})")
        sns.scatterplot(x=ind_elec_state_df[ind_elec_state_df['ac_type']==selected_category].electors,
                        y=ind_elec_state_df[ind_elec_state_df['ac_type']==selected_category].totvotpoll,
                        s=25,
                        hue= ind_elec_state_df.cand_sex);
        plt.xlabel('Registred Voters')
        plt.ylabel('Votes Received')
        plt.show()
assembly_dropdown.observe(scatterplot_category, names='value')

# Display widgets
display(assembly_dropdown)
display(output)


import ipywidgets as widgets
from IPython.display import display
import matplotlib.pyplot as plt
import seaborn as sns

# Assuming you have imported and prepared your DataFrame as 'ind_elec_national_df'

parliament_dropdown = widgets.Dropdown(options=ind_elec_national_df['pc_type'].unique(), description='Parliament Category')
output = widgets.Output()

def par_cat(change):
    selected_p_category = parliament_dropdown.value
    with output:
        output.clear_output()
        plt.figure(figsize=(10, 6))  # Adjust the figure size if needed
        plt.title(f"INDIAN NATIONAL ELECTION DATASET TOTAL VOTE POLL vs Number of registered voters ({selected_p_category})")
        sns.scatterplot(y='electors', x='totvotpoll', data=ind_elec_national_df[ind_elec_national_df['pc_type'] == selected_p_category], s=25, hue='cand_sex')
        plt.xlabel('Registered Voters')
        plt.ylabel('Votes Received')
        plt.legend(title='Candidate Sex')
        plt.show()

parliament_dropdown.observe(par_cat, names='value')
display(parliament_dropdown)
display(output)


plt.title("INDIAN ELECTION DATASET ELECTORS VS TOTTAL VOTE POLL",fontsize=14)
sns.scatterplot(y=ind_elec_state_df.electors,x=ind_elec_state_df.totvotpoll,
                hue=ind_elec_state_df.ac_type
                ,s=25);


# Loading the dataset
ind_elec_state_df =pd.read_csv('indian-state-level-election.csv')


import ipywidgets as widgets
from IPython.display import display
import matplotlib.pyplot as plt
import seaborn as sns

# Convert 'year' column to integers (replace this line with your actual DataFrame loading code)
ind_elec_state_df['year'] = ind_elec_state_df['year'].astype(int)

# Create year and partyname dropdown widgets
year_dropdown = widgets.Dropdown(options=ind_elec_state_df['year'].unique(), description='Select Year:')
partyname_dropdown = widgets.Dropdown(options=ind_elec_state_df['partyname'].unique(), description='Select Party:')

# Define an output widget
output = widgets.Output()

# Create a function to update the histogram based on the selected year and partyname
def update_histogram(change):
    selected_year = year_dropdown.value
    selected_partyname = partyname_dropdown.value
    
    with output:
        output.clear_output()
        
        # Filter the data based on the selected year and partyname
        filtered_data = ind_elec_state_df[
            (ind_elec_state_df['year'] == selected_year) &
            (ind_elec_state_df['partyname'] == selected_partyname)
        ]
        
        if filtered_data.empty:
            print("No data available to display.")
        else:
            plt.rcParams['font.size'] = 32
            plt.rcParams['figure.figsize'] = (25, 10)
            sns.set(font_scale=1.9)
    
            plt.hist(filtered_data['st_name'], bins=50)
            plt.title(f'Distribution of State Names For STATE ELECTION ({selected_year}, {selected_partyname})')
            plt.xlabel('States ---->')
            plt.ylabel("Total Vote Received ---->")
            plt.xticks(rotation=45, ha='right')
            plt.tight_layout()
            plt.show()

year_dropdown.observe(update_histogram, names='value')
partyname_dropdown.observe(update_histogram, names='value')

# Display widgets
display(widgets.HBox([year_dropdown, partyname_dropdown]))
display(output)


import ipywidgets as widgets
from IPython.display import display
import matplotlib.pyplot as plt
import seaborn as sns
# Convert 'year' column to integers
ind_elec_national_df['year'] = ind_elec_national_df['year'].astype(int)

# Create year and state dropdown widgets
year_dropdown = widgets.Dropdown(options=ind_elec_national_df['year'].unique(), description='Select Year:')
party_dropdown = widgets.Dropdown(options=ind_elec_national_df['partyname'].unique(), description='Select Party:')

# Define an output widget
output = widgets.Output()

# Create a function to update the histogram based on the selected year and state
def update_histogram(change):
    selected_year = year_dropdown.value
    selected_party = party_dropdown.value
    
    with output:
        output.clear_output()
        plt.rcParams['font.size'] = 32
        plt.rcParams['figure.figsize'] = (25, 10)
        sns.set(font_scale=1.9)

        filtered_data = ind_elec_national_df[
            (ind_elec_national_df['year'] == selected_year) &
            (ind_elec_national_df['partyname'] == selected_party)
        ]

        if filtered_data.empty:
            print("No data available to display.")
        else:
            plt.hist(filtered_data['st_name'], bins=50)
            plt.title(f'Distribution of State Names For National ELECTION ({selected_year}, {selected_party})')
            plt.xlabel('States ---->')
            plt.ylabel("Total Vote Received ---->")
            plt.xticks(rotation=45, ha='right')
            plt.tight_layout()
            plt.show()

year_dropdown.observe(update_histogram, names='value')
party_dropdown.observe(update_histogram, names='value')

# Display widgets
display(widgets.HBox([year_dropdown, party_dropdown]))
display(output)


ind_elec_state_df.ac_type.unique()

array(['GEN', 'ST', 'SC', 'BL', 'SANGH'], dtype=object)


ind_elec_national_df.pc_type.unique()

array(['GEN', nan, 'ST', 'SC', 'SC '], dtype=object)


GEN_df=ind_elec_state_df[ind_elec_state_df.ac_type=='GEN']
ST_df=ind_elec_state_df[ind_elec_state_df.ac_type=='ST']
SC_df=ind_elec_state_df[ind_elec_state_df.ac_type=='SC']
BL_df=ind_elec_state_df[ind_elec_state_df.ac_type=='BL']
SANGH_df=ind_elec_state_df[ind_elec_state_df.ac_type=='SANGH']


sns.set(font_scale=2.2)
plt.hist(GEN_df.ac_type,alpha=0.5,bins=np.arange(0,5,0.25));
plt.hist(ST_df.ac_type,alpha=0.5,bins=np.arange(0,5,0.25));
plt.hist(SC_df.ac_type,alpha=0.5,bins=np.arange(0,5,0.25));
plt.hist(BL_df.ac_type,alpha=0.5,bins=np.arange(0,5,0.25));
plt.hist(SANGH_df.ac_type,alpha=0.5,bins=np.arange(0,5,0.25));
plt.title('INDIAN STATE ELECTION DATASET AC Reservation VS ELECTORS')
plt.xlabel('Assembly constituency reservation status ---->')
plt.ylabel("Electors ---->")
plt.legend(['GEN', 'ST', 'SC', 'BL', 'SANGH']);


ind_elec_state_df.partyabbre.unique()

array(['IND', 'INC', 'INC(I)', ..., 'TRMRPPI', 'RJNP', 'JHAP'],
      dtype=object)


sns.set(font_scale=2.5)
plt.hist([GEN_df.ac_type,ST_df.ac_type,SC_df.ac_type,BL_df.ac_type,SANGH_df.ac_type],bins=np.arange(0,5,0.25),stacked=1);
plt.title('INDIAN ELECTION DATASET Assembly Category VS ELECTORS')
plt.xlabel('Ac type ---->')
plt.ylabel("Electors ---->")
plt.legend(['GEN', 'ST', 'SC', 'BL', 'SANGH']);


elec_year=ind_elec_state_df.year
acc_type=ind_elec_state_df.ac_type
party_name= ind_elec_state_df.partyabbre.unique()
print(elec_year ,acc_type,party_name)

0         1978
1         1978
2         1978
3         1978
4         1978
          ... 
327289    2011
327290    2011
327291    2011
327292    2011
327293    2011
Name: year, Length: 327294, dtype: int32 0         GEN
1         GEN
2         GEN
3         GEN
4         GEN
         ... 
327289    GEN
327290    GEN
327291    GEN
327292    GEN
327293    GEN
Name: ac_type, Length: 327294, dtype: object ['IND' 'INC' 'INC(I)' ... 'TRMRPPI' 'RJNP' 'JHAP']


matplotlib.rcParams['font.size'] = 10;
sns.set(font_scale=1.5)
sns.pairplot(ind_elec_state_df,hue='ac_type')
plt.title("Pairplot for state election Grouped by Reservation");


matplotlib.rcParams['font.size'] = 14;
sns.set(font_scale=1.5)
sns.pairplot(ind_elec_national_df,hue='pc_type')
plt.title("Pairplot for state election Grouped by Reservation");


sns.set(font_scale=1.5)
sns.pairplot(ind_elec_state_df,hue='cand_sex');
plt.title("Pairplot for state election Grouped by Gender");


sns.set(font_scale=1.5)
sns.pairplot(ind_elec_national_df,hue='cand_sex');
plt.title("Pairplot for national election Grouped by Gender")

Text(0.5, 1.0, 'Pairplot for national election Grouped by Gender')


ind_elec_state_df


ind_elec_national_df


# Finding the Outliers 
matplotlib.rcParams['font.size'] = 16;
matplotlib.rcParams['figure.figsize'] = (20, 10);
plt.title("Indian State Election")
sns.boxplot(ind_elec_state_df.totvotpoll,ind_elec_state_df.ac_type , hue=ind_elec_state_df.cand_sex);

C:\Users\Admin\anaconda3\lib\site-packages\seaborn\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(


# Finding the Outliers  
matplotlib.rcParams['font.size'] = 16;
matplotlib.rcParams['figure.figsize'] = (20, 10);
plt.title("Indian National Election")
sns.boxplot(ind_elec_national_df.totvotpoll,ind_elec_national_df.pc_type , hue=ind_elec_national_df.cand_sex);

C:\Users\Admin\anaconda3\lib\site-packages\seaborn\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(


ind_elec_state_df['totvotpoll'] = ind_elec_state_df['totvotpoll'].fillna(0)
ind_elec_state_df['totvotpoll'] = ind_elec_state_df['totvotpoll'].replace([np.inf, -np.inf], 999999)


ind_elec_state_df['totvotpoll'] = ind_elec_state_df['totvotpoll'].astype(int)


ind_elec_state_df.dtypes

st_name       object
year           int32
ac_no          int64
ac_name       object
ac_type       object
cand_name     object
cand_sex      object
partyname     object
partyabbre    object
totvotpoll     int32
electors       int64
dtype: object


ind_elec_state_df


from IPython.display import display
with pd.option_context('display.max_rows',7500):    
    display(ind_elec_state_df.groupby('ac_name')[['cand_name']].sum())


import ipywidgets as widgets
from IPython.display import display
import matplotlib.pyplot as plt
import seaborn as sns
party_dropdown = widgets.Dropdown(options = ind_elec_state_df['partyname'].unique(),description ='Select Party')
output = widgets.Output()
def sps(change):
    selected_party = party_dropdown.value
    with options:
        output.clear_output()
        sns.set_style('whitegrid')
        sns.set(font_scale=1.2)
        plt.figure(figsize=(12, 8))
        sns.scatterplot(x=ind_elec_state_df[ind_elec_state_df['partyname'].year],
                        y=ind_elec_state_df[ind_elec_state_df['partyname'].totvotpoll])
        plt.title(f'INDIAN ELECTION DATABASE SCATTERPLOT TOTAL VOTE POLL vs YEAR WISE of {selected_party}')
        plt.xticks(rotation=45)
        plt.tight_layout()
        plt.show()

party_dropdown.observe(update_histogram, names='value')

# Display widgets
display(widgets.VBox([party_dropdown]))
display(output)


ind_elec_state_df


import matplotlib.pyplot as plt
import seaborn as sns

# Set the figure size
plt.figure(figsize=(7.5, 5.8))

# Plot the distribution
sns.distplot(x=ind_elec_state_df['totvotpoll'], bins=10,kde = True)

# Add labels and title
plt.title('State Votes Distribution')
plt.xlabel('Total Votes Polled---->')
plt.ylabel('Density---->')

# Show the plot
plt.show();

C:\Users\Admin\anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)


import matplotlib.pyplot as plt
import seaborn as sns

# Set the figure size
plt.figure(figsize=(7.5, 5.8))

# Plot the distribution
sns.distplot(x=ind_elec_national_df['totvotpoll'], bins=10,kde = True)

# Add labels and title
plt.title('National Votes Distribution')
plt.xlabel('Total Votes Polled---->')
plt.ylabel('Density---->')

# Show the plot
plt.show();

C:\Users\Admin\anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)


import matplotlib.pyplot as plt
import seaborn as sns
plt.figure(figsize=(12, 8))
sns.set(style="whitegrid")  # Set a grid background

# Create a rug plot
sns.rugplot(x=ind_elec_state_df['totvotpoll'], height=0.5, color='blue')

plt.xlabel('Total Votes Polled', fontsize=14)
plt.ylabel('Frequency', fontsize=14)
plt.title('Rugplot Distribution of state Election', fontsize=12)

# Remove spines and adjust tick parameters
sns.despine(trim=True, left=True)
plt.xticks(fontsize=12)
plt.yticks(fontsize=12)

plt.tight_layout()
plt.show()


import matplotlib.pyplot as plt
import seaborn as sns
plt.figure(figsize=(12, 8))
sns.set(style="whitegrid",palette = 'coolwarm')  # Set a grid background

# Create a rug plot
sns.rugplot(x=ind_elec_national_df['totvotpoll'], height=0.5, color='blue')

plt.xlabel('Total Votes Polled', fontsize=14)
plt.ylabel('Frequency', fontsize=14)
plt.title('Rugplot Distribution of National Election', fontsize=12)

# Remove spines and adjust tick parameters
sns.despine(trim=True, left=True)
plt.xticks(fontsize=12)
plt.yticks(fontsize=12)

plt.tight_layout()
plt.show();


plt.figure(figsize=(12,4.8));
plt.title('STATE Registred Voters  VS Gender', fontsize=20);
sns.violinplot(y = ind_elec_state_df['electors'], x = ind_elec_state_df['cand_sex']);
plt.xlabel("Gender")
plt.ylabel("Registred Voters")

Text(0, 0.5, 'Registred Voters')


plt.figure(figsize=(12,4.8));
plt.title('National Registred Voters  VS Gender', fontsize=20);
sns.violinplot(y = ind_elec_national_df['electors'], x = ind_elec_national_df['cand_sex']);
plt.xlabel("Gender")
plt.ylabel("Registred Voters")
plt.show();

Text(0, 0.5, 'Registred Voters')


import numpy as np
import matplotlib.pyplot as plt
fig = plt.figure(figsize=(10,5))
plt.bar(ind_elec_state_df.ac_no,ind_elec_state_df.electors,color='maroon',width=0.15)
plt.xlabel("Assembly_no")
plt.ylabel("Registred Voters")
plt.title('STATE ELECTION Registred Voters vs Assembly No', fontsize=16)

plt.show();


import seaborn as sns
import matplotlib.pyplot as plt

sns.set_style('whitegrid')
sns.set(font_scale=1.2)

# Sort the DataFrame by the 'year' column
ind_elec_state_df_sorted = ind_elec_state_df.sort_values(by='year')

fig, ax = plt.subplots(figsize=(24, 8))
sns.barplot(x='year', y='electors', data=ind_elec_state_df_sorted, color='maroon', ax=ax)

ax.set_xlabel('Year', fontsize=20)
ax.set_ylabel('Number of Electors', fontsize=20)
ax.set_title('STATE ELECTION Registred Voter vs Year', fontsize=16)

plt.xticks(rotation=45, fontsize=16)
plt.tight_layout()
plt.show()


import seaborn as sns
import matplotlib.pyplot as plt

sns.set_style('whitegrid')
sns.set(font_scale=3.2)

fig, ax = plt.subplots(figsize=(50,25))
sns.boxplot(x='electors', y='year', data=ind_elec_state_df, dodge=True, ax=ax)
ax.set_xlabel('electors', fontsize=48)
ax.set_ylabel('Year', fontsize=48)
ax.set_title('Election in India by Candidate Sex', fontsize=46)
plt.show()


import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('whitegrid')
sns.set(font_scale=2.2)

fig, ax = plt.subplots(figsize=(24, 8))
sns.violinplot(x='year', y='electors', data=ind_elec_state_df)

ax.set_xlabel('Year', fontsize=20)
ax.set_ylabel('Registered Voters', fontsize=20)
ax.set_title('State Election in India', fontsize=24);

plt.xticks(rotation=45, fontsize=16)
plt.tight_layout()
plt.show()


import seaborn as sns
import matplotlib.pyplot as plt

sns.set(font_scale=2.2)
plt.figure(figsize=(25, 10))
# Change the order of 'year' and 'ac_type'

g = sns.FacetGrid(ind_elec_state_df, col='ac_type', row='year', height=6, aspect=1.2) 
g.map(sns.histplot, 'cand_sex')
for ax in g.axes.flat:
    ax.tick_params(axis='x', labelrotation=90)

plt.tight_layout()
plt.show()

<Figure size 1800x720 with 0 Axes>


import seaborn as sns
import matplotlib.pyplot as plt

# Set the font size of the plot
matplotlib.rcParams['font.size'] = 56

# Set the figure size
plt.figure(figsize=(20,10))
plt.title('SCATTERPLOT YEAR vs ELECTORS')

# Create a scatter plot of "year" versus "electors", with the color of the markers indicating "cand_sex"
sns.scatterplot(x="year", y="electors", data=ind_elec_state_df, hue="cand_sex")

# Rotate the x-axis labels by 45 degrees
plt.xticks(rotation=45)

# Show the plot
plt.show();


import seaborn as sns
import matplotlib.pyplot as plt

sns.set(font_scale=2.0)
plt.figure(figsize=(25, 10))

heatmap = sns.heatmap(ind_elec_state_df.isnull(), yticklabels=False, cbar=False, cmap='YlOrBr')
plt.title('Missing Values Heatmap', fontsize=36)

plt.show();


plt.bar(ind_elec_state_df.ac_type, ind_elec_state_df.electors, label='Number of Electors')
plt.title('Indian Election Dataset',fontsize=64)
plt.xlabel('AC Type',fontsize=64)
plt.ylabel('Electors',fontsize=64)
plt.legend()
plt.show()


import seaborn as sns
import matplotlib.pyplot as plt

sns.set(font_scale=1.4)
plt.figure(figsize=(8, 6))

# Countplot of candidate sex
plt.subplot(121)
sns.countplot(data=ind_elec_state_df, x='cand_sex')
plt.title('Countplot of Candidate Sex')

# Pie chart of candidate sex
plt.subplot(122)
plt.title('Pie Chart of Candidate Sex')
sex_counts = ind_elec_state_df['cand_sex'].value_counts()
labels = sex_counts.index.tolist()
plt.pie(sex_counts, labels=labels)

plt.tight_layout()
plt.show()


ind_elec_state_df.corr()


import matplotlib.pyplot as plt
import seaborn as sns

# Set the font scale for better readability
sns.set(font_scale=1.4)

# Create a correlation clustermap
clustermap = sns.clustermap(ind_elec_state_df[['totvotpoll','ac_no',  'electors']].corr(), cmap='coolwarm',annot = True)

# Add axis labels
plt.xlabel('Election Variables')
plt.ylabel('Election Variables')
plt.title("Cluster Graph of State Election")
# Display the clustermap
plt.show();


ind_elec_national_df = pd.read_csv('indian-national-level-election.csv')


ind_elec_national_df.corr()


import matplotlib.pyplot as plt
import seaborn as sns

# Set the font scale for better readability
sns.set(font_scale=1.4)

# Create a correlation clustermap
clustermap = sns.clustermap(ind_elec_national_df[['totvotpoll','pc_no',  'electors']].corr(), cmap='coolwarm',annot = True)

# Add axis labels
plt.xlabel('Election Variables')
plt.ylabel('Election Variables')
plt.title("Cluster Graph of National Election")

# Display the clustermap
plt.show();


ind_elec_state_df.shape

(327294, 11)


print("Total Number of winner candidate is {} in the table ".format(ind_elec_state_df.shape[0]))

Total Number of winner candidate is 327294 in the table


unique_values = ind_elec_state_df.nunique()
sum_of_unique_values = unique_values.sum()
seat_saver = ind_elec_state_df.shape[0] -sum_of_unique_values
seat_saver

12711


print("The Quality Candidate that Save their Seat in Next Election is {}".format(seat_saver))

The Quality Candidate that Save their Seat in Next Election is 12711


ind_elec_state_df.ac_no.value_counts()

118    2118
31     1901
51     1874
25     1872
23     1850
       ... 
409      62
406      61
404      61
415      56
425      29
Name: ac_no, Length: 425, dtype: int64


ind_elec_bihar_df = ind_elec_state_df[ind_elec_state_df['st_name'] == 'Bihar']
avg_vote_bihar = ind_elec_bihar_df['totvotpoll'].mean()
print('The average vote in Bihar is {}'.format(int(avg_vote_bihar)))

The average vote in Bihar is 6436


y=ind_elec_state_df[(ind_elec_state_df.electors >= 13500) & (ind_elec_state_df.electors <= 13500)].shape


print("Number of Registred Voters > 13500 is {}" .format(y[0]))

Number of Registred Voters > 13500 is 5


print("Number of Registred Voters < 13500 is {}" .format(y[1]))

Number of Registred Voters < 13500 is 11


max_sum = ind_elec_state_df['electors'].count().max()


max_sum

327294


max_value = ind_elec_state_df['electors'].max()
c = ind_elec_state_df[ind_elec_state_df['electors'] == max_value]

c


min_value = ind_elec_state_df[ind_elec_state_df['electors'] > 0]['electors'].min()
f = ind_elec_state_df[ind_elec_state_df['electors'] == min_value]


print("Here is the details of Candidates who got minimum elecotrs ")
f

Here is the details of Candidates who got minimum elecotrs


c.shape

(11, 11)


print("Total candidate is {}".format(c.shape[0]))

Total candidate is 11


max_value_total_vote_polled = ind_elec_state_df['totvotpoll'].max()
d = ind_elec_state_df[ind_elec_state_df['totvotpoll'] == max_value_total_vote_polled]


print("Here is the details of that candidate who gets maximum total vote polled")
d

Here is the details of that candidate who gets maximum total vote polled


min_value_total_vote_polled = ind_elec_state_df[ind_elec_state_df['totvotpoll'] > 0]['totvotpoll'].min()
e = ind_elec_state_df[ind_elec_state_df['totvotpoll'] == min_value_total_vote_polled]


print("Here is the details of that candidate who gets minimum total vote polled")

print("Total Candidate is {}".format(e.shape[0]))
e

Here is the details of that candidate who gets minimum total vote polled
Total Candidate is 182


male_count = ind_elec_state_df['cand_sex'].value_counts()['M']
female_count = ind_elec_state_df['cand_sex'].value_counts()['F']
third_gender = ind_elec_state_df['cand_sex'].value_counts()['O']
print("Number of Male candidates is {} & Female candidates is {} & Third_gender is {}: ".format(male_count, female_count,third_gender))

Number of Male candidates is 307098 & Female candidates is 14883 & Third_gender is 11:


ind_elec_state_df_group= ind_elec_state_df.groupby('ac_name')[['cand_name']].sum()
ind_elec_state_df_group


ind_elec_bihar_df=ind_elec_state_df[ind_elec_state_df.st_name=='Bihar']
ind_elec_bihar_df


ind_elec_bihar_df.dtypes

st_name       object
year           int32
ac_no          int64
ac_name       object
ac_type       object
cand_name     object
cand_sex      object
partyname     object
partyabbre    object
totvotpoll     int32
electors       int64
dtype: object


ind_elec_state_df.totvotpoll.sum()

3043662215


type(ind_elec_bihar_df.partyabbre)

pandas.core.series.Series


ind_elec_bihar_df.dtypes

st_name       object
year           int32
ac_no          int64
ac_name       object
ac_type       object
cand_name     object
cand_sex      object
partyname     object
partyabbre    object
totvotpoll     int32
electors       int64
dtype: object


ind_elec_bihar_df.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 38064 entries, 31684 to 69747
Data columns (total 11 columns):
 #   Column      Non-Null Count  Dtype 
---  ------      --------------  ----- 
 0   st_name     38064 non-null  object
 1   year        38064 non-null  int32 
 2   ac_no       38064 non-null  int64 
 3   ac_name     38064 non-null  object
 4   ac_type     38064 non-null  object
 5   cand_name   38064 non-null  object
 6   cand_sex    34541 non-null  object
 7   partyname   38064 non-null  object
 8   partyabbre  38064 non-null  object
 9   totvotpoll  38064 non-null  int32 
 10  electors    38064 non-null  int64 
dtypes: int32(2), int64(2), object(7)
memory usage: 3.2+ MB


sex_counts = ind_elec_state_df['cand_sex'].value_counts()
labels = sex_counts.index.tolist()
plt.pie(sex_counts, labels=labels, autopct='%1.3f%%',startangle = 20,explode=[0.1, 0.1,0.2],colors =['Crimson', 'Orchid','Coral']);


print("Number of Male candidates is {}  ".format(male_count))
print("Number of Female Candidate is {}".format(female_count))
print("Number of Third Gender Candidate is {}".format(third_gender))
sum_cand = male_count+ female_count+ third_gender
print("Total Candidate is {} ".format(sum_cand))

Number of Male candidates is 307098  
Number of Female Candidate is 14883
Number of Third Gender Candidate is 11
Total Candidate is 321992


sex_counts = ind_elec_national_df['cand_sex'].value_counts()
labels = sex_counts.index.tolist()
plt.pie(sex_counts, labels=labels, autopct='%1.3f%%',startangle = 20,explode=[0.1, 0.1,0.2],colors =['Crimson', 'Orchid','Coral']);


print("Number of Male candidates is {}  ".format(male_count))
print("Number of Female Candidate is {}".format(female_count))
print("Number of Third Gender Candidate is {}".format(third_gender))
sum_cand = male_count+ female_count+ third_gender
print("Total Candidate is {} ".format(sum_cand))

Number of Male candidates is 307098  
Number of Female Candidate is 14883
Number of Third Gender Candidate is 11
Total Candidate is 321992


import jovian


jovian.commit()

[jovian] Updating notebook "abhishekgupta022/indian-election-dataset" on https://jovian.com/
[jovian] Committed successfully! https://jovian.com/abhishekgupta022/indian-election-dataset

'https://jovian.com/abhishekgupta022/indian-election-dataset'

	st_name	year	ac_no	ac_name	ac_type	cand_name	cand_sex	partyname	partyabbre	totvotpoll	electors
159682	Maharashtra	1978.0	52	NaN	GEN	Patwardhan Prabhakar Ramchandar	M	Janata Party	JNP	56061.0	147865
122331	Karnataka	2008.0	92	Siruguppa	ST	Somalingappa M.S	M	BJP	BJP	43359.0	154602
213444	Rajasthan	1980.0	130	Ghatol	ST	Puji Lal	M	Indian National Congress (I)	INC(I)	18004.0	81814
6315	Andhra Pradesh	1989.0	201	Tandur	GEN	Pasaram Shanth Kumar	M	Telugu Desam	TDP	37422.0	134899
95025	Haryana	2009.0	59	Bawani Khera	SC	Ramkishan So Fattu	M	IND	IND	268.0	152436
303053	Uttar Pradesh	2007.0	232	Gyanpur	GEN	Ram Kishor	M	Lok Jan Shakti Party	LJP	1017.0	333543
144159	Madhya Pradesh	1990.0	241	Berasia	GEN	Batan Lal	M	Independents	IND	283.0	126295
154152	Madhya Pradesh	2008.0	44	Jatara	SC	Ahirwar Kamlapat	M	LJP	LJP	371.0	147322
282334	Uttar Pradesh	1993.0	114	Kunda	GEN	Rakesh Kumar	M	Independents	IND	27.0	199728
304508	Uttar Pradesh	2007.0	336	Bah	GEN	Shyam Baran	M	Independent	IND	945.0	277185

	year	ac_no	totvotpoll	electors
count	327294.000000	327294.000000	327287.000000	3.272940e+05
mean	1996.143765	127.910918	9299.673421	1.684289e+05
std	10.993383	96.626260	17025.780928	8.316493e+04
min	1977.000000	1.000000	0.000000	0.000000e+00
25%	1989.000000	49.000000	289.000000	1.186640e+05
50%	1995.000000	106.000000	1022.000000	1.578260e+05
75%	2006.000000	193.000000	9182.000000	2.010310e+05
max	2015.000000	425.000000	584098.000000	1.593907e+06

	st_name	year	pc_no	pc_name	pc_type	cand_name	cand_sex	partyname	partyabbre	totvotpoll	electors
0	Andaman & Nicobar Islands	1977	1	Andaman & Nicobar Islands	GEN	K.R. Ganesh	M	Independents	IND	25168	85308
1	Andaman & Nicobar Islands	1977	1	Andaman & Nicobar Islands	GEN	Manoranjan Bhakta	M	Indian National Congress	INC	35400	85308
2	Andaman & Nicobar Islands	1980	1	Andaman & Nicobar Islands	GEN	Ramesh Mazumdar	M	Independents	IND	109	96084
3	Andaman & Nicobar Islands	1980	1	Andaman & Nicobar Islands	GEN	Alagiri Swamy	M	Independents	IND	125	96084
4	Andaman & Nicobar Islands	1980	1	Andaman & Nicobar Islands	GEN	Kannu Chemy	M	Independents	IND	405	96084
...	...	...	...	...	...	...	...	...	...	...	...
73076	West Bengal	2014	42	Birbhum	GEN	None Of The Above	NaN	NOTA	NOTA	14557	1495089
73077	West Bengal	2014	42	Birbhum	GEN	Syed Siraj Jimmi	M	INC	INC	132084	1495089
73078	West Bengal	2014	42	Birbhum	GEN	Joy Banerjee	M	BJP	BJP	235753	1495089
73079	West Bengal	2014	42	Birbhum	GEN	Dr. Elahi Kamre Mahammad	M	CPM	CPM	393305	1495089
73080	West Bengal	2014	42	Birbhum	GEN	Satabdi Roy	F	AITC	AITC	460568	1495089

	st_name	year	pc_no	pc_name	pc_type	cand_name	cand_sex	partyname	partyabbre	totvotpoll	electors
65775	Uttar Pradesh	2004	29	Gonda	GEN	Suresh Chandra Tripathi Advocate	M	CPI	CPI	6002	1391430
54628	Uttar Pradesh	1977	38	Gorakhpur	GEN	Harish Chandra	M	Independents	IND	3020	570532
72301	West Bengal	2009	12	Krishnanagar	NaN	Jayasri Chakrabarty	F	Samajwadi Party	SP	5002	1223082
31563	Madhya Pradesh	1998	37	Indore	GEN	Jagdish Bhai Patidar	M	Shivsena	SHS	783	1423528
7003	Bihar	1980	14	Jhanjharpur	GEN	Shafiqulla	M	Independents	IND	987	700394

	st_name	year	pc_name	pc_type	cand_name	cand_sex	partyname	totvotpoll	electors
0	Andaman & Nicobar Islands	1977	Andaman & Nicobar Islands	GEN	K.R. Ganesh	M	Independents	25168	85308
1	Andaman & Nicobar Islands	1977	Andaman & Nicobar Islands	GEN	Manoranjan Bhakta	M	Indian National Congress	35400	85308
2	Andaman & Nicobar Islands	1980	Andaman & Nicobar Islands	GEN	Ramesh Mazumdar	M	Independents	109	96084
3	Andaman & Nicobar Islands	1980	Andaman & Nicobar Islands	GEN	Alagiri Swamy	M	Independents	125	96084
4	Andaman & Nicobar Islands	1980	Andaman & Nicobar Islands	GEN	Kannu Chemy	M	Independents	405	96084
...	...	...	...	...	...	...	...	...	...
73076	West Bengal	2014	Birbhum	GEN	None Of The Above	NaN	NOTA	14557	1495089
73077	West Bengal	2014	Birbhum	GEN	Syed Siraj Jimmi	M	INC	132084	1495089
73078	West Bengal	2014	Birbhum	GEN	Joy Banerjee	M	BJP	235753	1495089
73079	West Bengal	2014	Birbhum	GEN	Dr. Elahi Kamre Mahammad	M	CPM	393305	1495089
73080	West Bengal	2014	Birbhum	GEN	Satabdi Roy	F	AITC	460568	1495089

INDIAN ELECTION DATASET¶

INTRODUCTION¶

About Dataset :¶

About- Project¶

Dataset - Source:¶

Data Cleaning and Info of State Level Elections¶

State election Variables¶

Filtering the state dataset¶

Data Cleaning and Info of National Level Elections¶

National Election Variables¶

Filtering the Natioal Election Dataset¶

EXPLORATORY DATA ANALYSIS AND VISUALISATION¶

Graphical Analysis for Visualisation¶

Q/A Exploration¶

Inferences and Conclusion¶

References and Future Work¶

	st_name	year	ac_no	ac_name	ac_type	cand_name	cand_sex	partyname	partyabbre	totvotpoll	electors
0	Andhra Pradesh	1978.0	1	Ichapuram	GEN	Manabala Ramarao	M	Independent	IND	813.0	83247
1	Andhra Pradesh	1978.0	1	Ichapuram	GEN	Appadu Sahu	M	Independent	IND	1743.0	83247
2	Andhra Pradesh	1978.0	1	Ichapuram	GEN	Uppada Rangababu	M	Indian National Congress	INC	4427.0	83247
3	Andhra Pradesh	1978.0	1	Ichapuram	GEN	Kalla Balarama Swamy	M	Indian National Congress (I)	INC(I)	19805.0	83247
4	Andhra Pradesh	1978.0	1	Ichapuram	GEN	Bendalam Venkatesam Sarma	M	Janata Party	JNP	34251.0	83247
...	...	...	...	...	...	...	...	...	...	...	...
327289	West Bengal	2011.0	294	Murarai	GEN	Ajrail Md	M	IND	IND	2471.0	189320
327290	West Bengal	2011.0	294	Murarai	GEN	Shiblal Sardar	M	BSP	BSP	3074.0	189320
327291	West Bengal	2011.0	294	Murarai	GEN	Sudhiranjan Das Goswami	M	BJP	BJP	4961.0	189320
327292	West Bengal	2011.0	294	Murarai	GEN	Elahi Kamre Dr	M	CPM	CPM	73414.0	189320
327293	West Bengal	2011.0	294	Murarai	GEN	Nure Alam Chowdhury	M	AITC	AITC	77817.0	189320

	st_name	year	ac_no	ac_name	ac_type	cand_name	cand_sex	partyname	partyabbre	totvotpoll	electors
0	Andhra Pradesh	1978	1	Ichapuram	GEN	Manabala Ramarao	M	Independent	IND	813.0	83247
1	Andhra Pradesh	1978	1	Ichapuram	GEN	Appadu Sahu	M	Independent	IND	1743.0	83247
2	Andhra Pradesh	1978	1	Ichapuram	GEN	Uppada Rangababu	M	Indian National Congress	INC	4427.0	83247
3	Andhra Pradesh	1978	1	Ichapuram	GEN	Kalla Balarama Swamy	M	Indian National Congress (I)	INC(I)	19805.0	83247
4	Andhra Pradesh	1978	1	Ichapuram	GEN	Bendalam Venkatesam Sarma	M	Janata Party	JNP	34251.0	83247
...	...	...	...	...	...	...	...	...	...	...	...
327289	West Bengal	2011	294	Murarai	GEN	Ajrail Md	M	IND	IND	2471.0	189320
327290	West Bengal	2011	294	Murarai	GEN	Shiblal Sardar	M	BSP	BSP	3074.0	189320
327291	West Bengal	2011	294	Murarai	GEN	Sudhiranjan Das Goswami	M	BJP	BJP	4961.0	189320
327292	West Bengal	2011	294	Murarai	GEN	Elahi Kamre Dr	M	CPM	CPM	73414.0	189320
327293	West Bengal	2011	294	Murarai	GEN	Nure Alam Chowdhury	M	AITC	AITC	77817.0	189320

	year	ac_no	totvotpoll	electors
year	1.000000	-0.115893	0.090591	0.504180
ac_no	-0.115893	1.000000	0.001548	0.203719
totvotpoll	0.090591	0.001548	1.000000	0.080355
electors	0.504180	0.203719	0.080355	1.000000

	year	pc_no	totvotpoll	electors
year	1.000000	-0.065162	0.053170	0.727499
pc_no	-0.065162	1.000000	-0.038903	0.047419
totvotpoll	0.053170	-0.038903	1.000000	0.033142
electors	0.727499	0.047419	0.033142	1.000000

	st_name	year	ac_no	ac_name	ac_type	cand_name	cand_sex	partyname	partyabbre	totvotpoll	electors
83002	Gujarat	2007	172	Chorasi	GEN	Ahir Manubhai Mayabhai	M	Independent	IND	818	1593907
83003	Gujarat	2007	172	Chorasi	GEN	Anna Yashwant Dharmraj Patil	M	Peoples Republican Party	PRBP	1027	1593907
83004	Gujarat	2007	172	Chorasi	GEN	Khan Hinabegum Kamruddin	F	Independent	IND	1330	1593907
83005	Gujarat	2007	172	Chorasi	GEN	Kotadiya Pravinbhai Dhirubhai	M	Independent	IND	1331	1593907
83006	Gujarat	2007	172	Chorasi	GEN	Anandbhai Keshavbhai Makwana	M	Independent	IND	1376	1593907
83007	Gujarat	2007	172	Chorasi	GEN	Vadivakar Avinash Dasharatbhai	M	Shivsena	SHS	2260	1593907
83008	Gujarat	2007	172	Chorasi	GEN	Naria Hansaben Vrajlalbhai	F	Lok Jan Shakti Party	LJP	4040	1593907
83009	Gujarat	2007	172	Chorasi	GEN	Patil Shubhash Narottam	M	Independent	IND	4409	1593907
83010	Gujarat	2007	172	Chorasi	GEN	Satyendra Sinh(Krantikari)	M	Independent	IND	13551	1593907
83011	Gujarat	2007	172	Chorasi	GEN	Dhanani Janakbhai Govindbhai	M	Indian National Congress	INC	237158	1593907
83012	Gujarat	2007	172	Chorasi	GEN	Narottambhai Patel	M	Bharatiya Janata Party	BJP	584098	1593907

	st_name	year	ac_no	ac_name	ac_type	cand_name	cand_sex	partyname	partyabbre	totvotpoll	electors
228593	Sikkim	1979	32	Sangha	GEN	Pema Lama	M	Sikkim Prajatantra Congress	SPC	45	2114
228594	Sikkim	1979	32	Sangha	GEN	Lhachen Ganchen Rimpuchhi	M	Independents	IND	733	2114

	st_name	year	ac_no	ac_name	ac_type	cand_name	cand_sex	partyname	partyabbre	totvotpoll	electors
24689	Assam	1983	109	Bihpuria	GEN	Biswanarayan Shastri	M	Independents	IND	1	64000
24705	Assam	1983	112	Dhakuakhana	ST	Anandra Pegu	M	Independents	IND	1	76877
24714	Assam	1983	113	Dhemaji	ST	Dimbeswar Doley	M	Independents	IND	1	90441
24715	Assam	1983	113	Dhemaji	ST	Dimbeswar Saikia	M	Independents	IND	1	90441
24716	Assam	1983	113	Dhemaji	ST	Mukt Ram Pegu	M	Independents	IND	1	90441
...	...	...	...	...	...	...	...	...	...	...	...
241571	Tamil Nadu	1996	118	Modakurichi	GEN	Sellamuthu, N.	M	Independent	IND	1	194579
280422	Uttar Pradesh	1993	33	Moradabad	GEN	Anil	M	Independents	IND	1	218386
280423	Uttar Pradesh	1993	33	Moradabad	GEN	Moh. Mustaqeen	M	Independents	IND	1	218386
288070	Uttar Pradesh	1993	363	Mathura	GEN	Tejveer Singh	M	Independents	IND	1	220372
288897	Uttar Pradesh	1993	397	Meerut	GEN	Prem Chand	M	Independents	IND	1	228504

	cand_name
ac_name
101. Jalna	Thombre Ananda LimbajiZiyauddin Ahemed QuadriJ...
Aawntui	R. VanlawmaR. DotinaiaThanglawraRobert Lalchhu...
Abdasa	Shah Lakhamshi PremjiKunverji Kanji ThackerNav...
Abhanpur	Shekhu Ram BanjareHemant Kumar TandonTikendra ...
Abhayapuri North	Aswini Kumar RoyAbdul HakimBasiruddin AhmedMah...
...	...
Ziradei	Rafiuddin MianShankar Nath VidyarathiNasiru Ra...
Ziro	Hage HaleyBengia HemantaTaso GrayuTanya SalaGr...
Ziro Hapoli	Sri Subu KoyangShri Kuru TaiNani RibiaShri Pad...
Ziro-Hapoli	Gyati TakkaNani RibyaPadi YubbePadi YubbeKuru ...
Zunheboto	KhuvisheS. Hukavi ZhimomiDr. K.C. NihosheS. Sh...

	st_name	year	ac_no	ac_name	ac_type	cand_name	cand_sex	partyname	partyabbre	totvotpoll	electors
31684	Bihar	1977	1	Dhanaha	GEN	Yogendra Pd. Srivastava	M	Janata Party	JNP	16746	86882
31685	Bihar	1977	1	Dhanaha	GEN	Har Deo Prasad	M	Indian National Congress	INC	16870	86882
31686	Bihar	1977	2	Bagha	SC	Purn Vashi	M	Independents	IND	1919	109756
31687	Bihar	1977	2	Bagha	SC	Kanchan Baitha	M	Janata Party	JNP	18825	109756
31688	Bihar	1977	2	Bagha	SC	Narsingh Baitha	M	Indian National Congress	INC	32058	109756
...	...	...	...	...	...	...	...	...	...	...	...
69743	Bihar	2010	243	Chakai	GEN	Nepali Singh	NaN	Nationalist Congress Party	NCP	8506	228938
69744	Bihar	2010	243	Chakai	GEN	Prithwi Raj Hembrom	NaN	Bahujan Samaj Party	BSP	16427	228938
69745	Bihar	2010	243	Chakai	GEN	Phalguni Prasad Yadav	NaN	Bharatiya Janata Party	BJP	20261	228938
69746	Bihar	2010	243	Chakai	GEN	Bijay Kumar Singh	NaN	Lok Jan Shakti Party	LJP	21621	228938
69747	Bihar	2010	243	Chakai	GEN	Sumit Kumar Singh	NaN	Jharkhand Mukti Morcha	JMM	21809	228938

	Unique Assembly Name
0	Ichapuram
1	Sompeta
2	Tekkali
3	Harishchandra
4	Narasannapeta
5	Patapatnam
6	Kothuru
7	Naguru
8	Parvathipuram
9	Salur
10	Bobbili
11	Therlam
12	Vunukuru
13	Palakonda
14	Amadalavalasa
15	Srikakulam
16	Etcherla
17	Cheepurupalli
18	Gajapathinagaram
19	Vizianagaram
20	Sathivada
21	Bhogapuram
22	Bheemunipatnam
23	Visakhapatnam-I
24	Visakhapatnam-Ii
25	Pendurthi
26	Uttarapalli
27	Srungavarapukota
28	Paderu
29	Madugula
30	Chodavaram
31	Anakapalli
32	Paravada
33	Elamanchili
34	Payakaraopeta
35	Narsipatnam
36	Chintapalli
37	Yellavaram
38	Burugupudi
39	Rajahmundry

	cand_name
ac_name
101. Jalna	Thombre Ananda LimbajiZiyauddin Ahemed QuadriJ...
Aawntui	R. VanlawmaR. DotinaiaThanglawraRobert Lalchhu...
Abdasa	Shah Lakhamshi PremjiKunverji Kanji ThackerNav...
Abhanpur	Shekhu Ram BanjareHemant Kumar TandonTikendra ...
Abhayapuri North	Aswini Kumar RoyAbdul HakimBasiruddin AhmedMah...
Abhayapuri South	Hem Charan SutradharAyodhya Ram DasRabindra Na...
Abohar	Bhup SinghParmanandRadha KrishanBrij LalThandu...
Aboi	B.P. HanghuEshakNyeiwangTaiwangC. Manpon Konya...
Achalpur	Prof. Ravindra Shamrao RaneGanorkar Sushiel Vi...
Achampet	Mahendranath PuttapagaPuttapaga RadhakrishnaR....
Achampet	Chinthala VenkateshwarluB.RavinderNone Of The ...
Achanta	Dasari PerumalluDidupatti Sundrara RajuKota Dh...
Acharapakkam	T. AyyavooR. KuppuswamyN. MuthuvelEthrajManava...
Adampur	Jai SinghJagdish ChanderSuraj BhanRam SinghMad...
Adampur	Sh. Santokh SinghSh. Rajvir SinghSh. Rajinder ...
Adapur	Ranjit SinghBhagirath ThakurPrahlad Prasad Yad...
Adarsh Nagar	Balraj GulatiS.P. GuptaRam ChanderRajiv KumarS...
Addanki	Dirisala Rajagopala ReddyChenchugarataiaha Bac...
Addanki	Lakshmireddy DevarapalliDupati YesobuKore Sure...
Adilabad	Vugge NamdevSyed Mudakkir ShahMasood Ahmsd Khu...
Adoni	N. IssacHarischandra ReddyNarsannaGattu Abdul ...
Adoor	N. RamanujanThottathil MunraliP.V. MathewMathe...
Adyar	Dr. Bhashkrao JibhakateDeshbhratar Dindayal Ka...
Afzalgarh	Abdul HaqBhagwan SinghVir ChandFateh Chand She...
Afzalpur	Babu RaoHanmath Rao DesaiM.Y.PatilMahantappa B...
Agar	Prakash KharateSatyanarayan JatiyaRamlal Malvi...
Agar	Ramlal MalviyaBabulalGangaramKailash ChandraNo...
Agartala	Lalit Mohan GoswamiShibani BhowmikMilan Chakra...
Aghunato	PukhayiTokhehoHukiyePukhayi
Agiaon	Ramdeo PremjiwanVimlesh RamRam Prasad RamSudha...
Agota	Jiwan LalJagjit SinghDaleepGhan ShiamVijai Pra...
Agra Cantonment	Kalyan DasMajor Harish Chandra UpadhayaD.D. Sa...
Agra Cantt.	Darab SinghRavi Kumar NivoriaHari KishanKhamra...
Agra East	Om Prakash KatauliaMohan Lal AgarwalBankey Beh...
Agra North	Krishan Gopal BaghelKarishma GoswamiCharan Sin...
Agra Rural	AshokKanhi SinghAmbedkari Hasanuram AmbedkariG...
Agra South	Sunita MahajanKamal AgrawalJaved KhanRam Brij ...
Agra West	Kharag SinghGaya PrasadChandrabhan AzadChandra...
Aheri	Atram Pramod RamaGawade Venkati PentaTalandi B...